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Artificial Neural Network for Estimation of Physical Parameters of Sea Water using LiDAR Waveforms

Saad Ahmed Jamal

TL;DR

This research proposed a novel solution based on neural networks for parameter estimation in LIDAR data analysis that successfully learned the inversion model, was able to do prediction of parameters such as depth, attenuation coefficient, and bottom reflectance.

Abstract

Light Detection and Ranging (LiDAR) are fast emerging sensors in the field of Earth Observation. It is a remote sensing technology that utilizes laser beams to measure distances and create detailed three-dimensional representations of objects and environments. The potential of Full Waveform LiDAR is much greater than just height estimation and 3D reconstruction only. Overall shape of signal provides important information about properties of water body. However, the shape of FWL is unexplored as most LiDAR software work on point cloud by utilizing the maximum value within the waveform. Existing techniques in the field of LiDAR data analysis include depth estimation through inverse modeling and regression of logarithmic intensity and depth for approximating the attenuation coefficient. However, these methods suffer from limitations in accuracy. Depth estimation through inverse modeling provides only approximate values and does not account for variations in surface properties, while the regression approach for the attenuation coefficient is only able to generalize a value through several data points which lacks precision and may lead to significant errors in estimation. Additionally, there is currently no established modeling method available for predicting bottom reflectance. This research proposed a novel solution based on neural networks for parameter estimation in LIDAR data analysis. By leveraging the power of neural networks, the proposed solution successfully learned the inversion model, was able to do prediction of parameters such as depth, attenuation coefficient, and bottom reflectance. Performance of model was validated by testing it on real LiDAR data. In future, more data availability would enable more accuracy and reliability of such models.

Artificial Neural Network for Estimation of Physical Parameters of Sea Water using LiDAR Waveforms

TL;DR

This research proposed a novel solution based on neural networks for parameter estimation in LIDAR data analysis that successfully learned the inversion model, was able to do prediction of parameters such as depth, attenuation coefficient, and bottom reflectance.

Abstract

Light Detection and Ranging (LiDAR) are fast emerging sensors in the field of Earth Observation. It is a remote sensing technology that utilizes laser beams to measure distances and create detailed three-dimensional representations of objects and environments. The potential of Full Waveform LiDAR is much greater than just height estimation and 3D reconstruction only. Overall shape of signal provides important information about properties of water body. However, the shape of FWL is unexplored as most LiDAR software work on point cloud by utilizing the maximum value within the waveform. Existing techniques in the field of LiDAR data analysis include depth estimation through inverse modeling and regression of logarithmic intensity and depth for approximating the attenuation coefficient. However, these methods suffer from limitations in accuracy. Depth estimation through inverse modeling provides only approximate values and does not account for variations in surface properties, while the regression approach for the attenuation coefficient is only able to generalize a value through several data points which lacks precision and may lead to significant errors in estimation. Additionally, there is currently no established modeling method available for predicting bottom reflectance. This research proposed a novel solution based on neural networks for parameter estimation in LIDAR data analysis. By leveraging the power of neural networks, the proposed solution successfully learned the inversion model, was able to do prediction of parameters such as depth, attenuation coefficient, and bottom reflectance. Performance of model was validated by testing it on real LiDAR data. In future, more data availability would enable more accuracy and reliability of such models.
Paper Structure (25 sections, 5 equations, 9 figures, 5 tables)

This paper contains 25 sections, 5 equations, 9 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Results and Discussion
  4. Conclusion
  5. Authorship contribution
  6. Conflicts of Interest
  7. Acknowledgement
  8. Data Availability

Figures (9)

  • Figure 1: The study area map shows the geographical location of the data collection area generated using https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview a license provided by the University of Salzburg.
  • Figure 2: Methodological Flowchart
  • Figure 3: Sample plots of simulated Waveforms generated through WALID
  • Figure 4: Difference between Surface and Sea-bed points
  • Figure 5: Attenuation coefficient assessment through regression
  • ...and 4 more figures